Scalable Techniques for Memory-efficient CDN Simulations

Since CDN simulations are known to be highly memory-intensive, in this paper, the authors argue the need for reducing the memory requirements of such simulations. They propose a novel memory-efficient data structure that stores cache state for a small subset of popular objects accurately and uses approximations for storing the state for the remaining objects. Since popular objects receive a large fraction of the requests while less frequently accessed objects consume much of the memory space, this approach yields large memory savings and reduces errors.